There have been many efforts to alter the metabolic characteristics of cells or strains in the desired direction by introducing new metabolic pathways or deleting, amplifying or modifying the existing metabolic pathways using molecular biological technology related to genetic recombination technology. With the aid of bioinformatics, which has been newly developed and increasingly used, the construction of each metabolic network model became possible, and thus it became possible to improve organisms to have various characteristics, including the overproduction of existing metabolites, the production of novel metabolites, inhibition of production of unfavorable metabolites, utilization of various substrates, degradation of non-biodegradable compounds.
However, currently, improvement of strains is performed mainly by methods, such as the over-expression of one or two enzymes or the introduction or deletion of simple metabolic pathways, but in many cases, the results was not as good as desired. In addition, metabolically improved strains can hardly be used in the production of substances that require changes in complex metabolic fluxes. It is known that one reason is because strains themselves generally tend to grow rather than to produce desired useful substances. Specifically, because strains have evolved that they would synthesize substances required for the growth of the strains themselves in the most optimized way, efforts to produce specific useful substances inevitably compete with these strains having a tendency to grow.
Another reason why the theoretical yield is not achieved is that complex metabolic pathways could not be correctly understood. Specifically, genetic recombination technology for the manipulation of metabolic pathways and the introduction of metabolic pathways has been significantly developed, whereas techniques for analysis and prediction through metabolic pathways have just recently showed the possibility with rapidly increasing genomic information. In particular, the metabolic pathway model of each of microorganisms is combined with mathematical models and optimization technology, and thus it is becoming possible to predict metabolic pathway reactions occurring after the deletion or addition of genes (Lee et al., Trends Biotechnol., 23:349, 2005).
It is known that metabolic flux analysis techniques show the ideal metabolic fluxes of cells and allow exact simulation and prediction of the behavior of cells, even though they do not require dynamic information (Papin, J. et al., Nature Reviews Molecular Cell Biology, 6:99, 2005). Metabolic flux analysis aims to determine an ideal metabolic flux space that cells can reach using only mass balance of biochemical reactions and information on cell composition, and to maximize or minimize specific objective functions through an optimization method (e.g., the maximization of biomass formation rate or the minimization of metabolic regulation by specific perturbation). In addition, metabolic flux analysis can be generally used to calculate the maximum production yield of the desired metabolite through strain improvement and the determined value can be used to understand the characteristics of metabolic pathways in strains. Also, various studies, which utilize the metabolic flux analysis technique to predict metabolic flux changes occurring after the deletion or addition of genes, have been reported.
In view of this, there is an urgent need to develop a method, which can explain the complex metabolism of microorganisms using the metabolic flux analysis technique from an overall point of view other than strain manipulation that uses partial metabolic information, which can provide an understanding of the effects of manipulation of a specific gene on the overall metabolic flux, and which can scientifically test and accurately predict the optimal microbial metabolic fluxes required for the mass production of target useful substances.
Accordingly, the present inventors have made many efforts to find a method for efficiently increasing the production of target useful substances, and as a result, found that specific key metabolites involved in the production of the useful substances can be identified by plotting a profile of an objective function through an algorithm that perturbs functions involved in the formation rate and production of the useful substances, determining the utilization (defined as flux sum (Φ)) of each metabolite from the profile, and screening key metabolites, from the profile. The key metabolites that show an increase in flux sum (Φ) value according to an increase in the formation rate of the useful substances, thereby complete the present invention.